• Journal of Institute of Control, Robotics and Systems
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• v.5 no.1
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• pp.62-68
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• 1999

The attitude of a vehicle can be precisely determined using GPS carrier phase measurements from more than two antennas attached to a vehicle and an efficient integer ambiguity resolution technique. Many methods utilizing the known baseline length as a constraint of independent elements of integer ambiguities are proposed to resolve integer ambiguity at real time. Three-dimensional search space is reduced to two-dimensional search space with this constraint. Thus the true integer ambiguity can be easily determined with less computational burden and fewer number of measurements. But there are still strong requirements for the real time integer ambiguity resolution, which uses single epoch measurement of long baseline. In this paper, a new constraint from the geometry of multiple baselines is derived. With this new constraint, two-dimensional search space is further reduced to one-dimensional search space. It makes possible to determine integer ambiguity with single epoch measurement. The proposed method is applied to real data to show its effectiveness.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.333-343
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• 2006

In this paper, the baseline-length information is directly modeled as a measurement for the Wald test, which speeds up the resolution convergence of the integer ambiguity of GPS carrier phase measurements. The convergent speed improvement is demonstrated using numerical simulation and real experiments. It is also shown that the integer ambiguities can be resolved using only four actual satellite measurements with very reasonable convergence speed, if the baseline-length information is used just like one additional observable satellite measurement. Finally, it is shown that the improvement of convergence speed of the Wald test is due to the increase of the probability ratio with the use of the baseline-length constraint.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.10-26
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• 2005

In our model, we keep inventory to satisfy uncertain demands which arrives irregularly. In this situation, we have additional two constraints. First, we need to have certain amount of order consolidation (consolidation constraint) for the orders to replenish the inventory because of production or purchase constraint. And also, if we order at a certain date which was set by administrative convenience, we have amount constraint to order the consolidated order demands (capacity constraint). We showed this variant inventory policy is needed in steel industry and note that there will be possible similar case in industry. To deal with this case, we invented a variant replenishment policy and show this policy is superior to other possible polices in the consolidation constraint case by extensive simulation. And we derive a combined solution method for dealing with the capacity constraints in addition to the consolidation constraints. For this, we suggest a combined solution method of integer programming and simulation.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.2
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• pp.99-112
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• 2006

In our model, we keep inventory to satisfy uncertain demands which arrives irregularly. In this situation, we have additional two constraints. First, we need to have certain amount of order consolidation (consolidation constraint) for the orders to replenish the inventory because of production or purchase amount constraint. And also, if we order at a certain date which was set by administrative convenience, we have capacity constraint to order the consolidated order demands (capacity constraint). We show this variant inventory policy is needed in steel industry and note that there will be possible similar case in industry. To deal with this case, we invent a variant replenishment policy and show this policy is superior to other possible polices in the consolidation constraint case by extensive simulation. And we derive a combined solution method for dealing with the capacity constraints in addition to the consolidation constraints. For this, we suggest a combined solution method of integer programming and simulation.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.47-55
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• 2010

Linear constraint satisfaction optimization problem is a kind of combinatorial optimization problem involving linearly expressed objective function and complex constraints. Integer programming is known as a very effective technique for such problem but require very much time and memory until finding a suboptimal solution. In this paper, we propose a method to improve the search performance by integrating local search and integer programming. Basically, simple hill-climbing search, which is the simplest form of local search, is used to solve the given problem and integer programming is applied to generate a neighbor solution. In addition, constraint programming is used to generate an initial solution. Through the experimental results using N-Queens maximization problems, we confirmed that the proposed method can produce far better solutions than any other search methods.

• Korean Management Science Review
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• v.25 no.1
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• pp.43-53
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• 2008

This paper considers a profit-based unit commitment problem with fuel consumption constraint and maintenance cost, which is one of the key decision problems in electricity industry. The nature of non-linearity inherent in the constraints and objective functions makes the problem intractable which have led many researches to focus on Lagrangian based heuristics. To solve the problem more effectively, we propose mixed integer programming based solution algorithm linearizing the complex non-linear constraints and objectives functions. The computational experiments using the real-world operation data taken from a domestic electricity power generator show that the proposed algorithm solves the given problem effectively.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.3
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• pp.17-23
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• 2008

In this research article, scheduling a casting sequence in a job-shop type foundry involving a variety of casts made of an identical alloy but with different shapes and II weights, has been investigated. The objective is to produce the assigned mixed orders satisfying due dates and obtaining the highest ingot efficiency simultaneously. Implementing simple integer programming instead of complicated genetic algorithms accompanying rigorous calculations proves that it can provide a feasible solution with a high accuracy for a complex, multi-variable and multi-constraint optimization problem. Enhancing the ingot efficiency under the constraint of discrete ingot sizes is accomplished by using a simple and intelligible algorithm in a standard integer programming. Employing this simple methodology, a job-shop type foundry is able to maximize the furnace utilization and minimize ingot waste.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.227-236
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• 1996

In this paper an algorithm based on cutting plane approach is developed which constructs all the efficient p-tuples of multiobjective integer linear fractional programming problem. The integer solution is constrained to satisfy and h out of n additional constraint sets. A numerical illustration in support of the proposed algorithm is developed.

• Journal of the Korea Society of Computer and Information
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• v.22 no.9
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• pp.9-16
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• 2017

The course timetabling problem is a problem assigning a set of subjects to the given classrooms and different timeslots, while satisfying various hard constraints and soft constraints. This problem is defined as a constraint satisfaction optimization problem and is known as an NP-complete problem. Various methods has been proposed such as integer programming, constraint programming and local search methods to solve a variety of course timetabling problems. In this paper, we propose an iterative improvement search method to solve the problem based on constraint programming. First, an initial solution satisfying all the hard constraints is obtained by constraint programming, and then the solution is repeatedly improved using constraint programming again by adding new constraints to improve the quality of the soft constraints. Through experimental results, we confirmed that the proposed method can find far better solutions in a shorter time than the manual method.

• Journal of Advanced Navigation Technology
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• v.11 no.4
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• pp.388-393
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• 2007

In designing transform coders bit allocation is one of the important issues. In this paper we propose two optimal algorithms for integer bit allocation in transform coding. Based on high-resolution formulas for bit allocation, the most and least greedy algorithms are developed to optimally adjust non-integer bit rates of coefficient quantizers to integer values. In particular, a duality property is observed between the two greedy algorithms.